Anomaly Detection in Network Traffic Using Machine Learning Techniques

Thesis Abstract

Abstract
The exponential growth in network traffic has led to an increase in security threats and network anomalies. Anomaly detection in network traffic plays a crucial role in ensuring the security and stability of network systems. This research project focuses on utilizing machine learning techniques for the effective detection of anomalies in network traffic. The primary objective of this study is to develop a robust anomaly detection system that can accurately identify and classify anomalies in real-time network traffic data. Chapter 1 provides an introduction to the research topic, background information on network traffic anomalies, the problem statement, objectives of the study, limitations, scope, significance, structure of the thesis, and definitions of key terms. The chapter sets the stage for understanding the importance of anomaly detection in network traffic and outlines the goals and framework of the research. Chapter 2 presents a comprehensive literature review that explores existing research and methodologies related to anomaly detection in network traffic. The review covers various machine learning techniques, algorithms, and tools that have been used in the field of anomaly detection. It analyzes the strengths and weaknesses of different approaches and highlights current trends and challenges in anomaly detection in network traffic. Chapter 3 details the research methodology employed in this study. It includes the research design, data collection methods, data preprocessing techniques, feature selection, machine learning algorithms, model evaluation metrics, and experimental setup. The chapter provides a step-by-step guide to how the research was conducted and explains the rationale behind the chosen methodologies. Chapter 4 presents a detailed discussion of the findings obtained from the experiments conducted in this research. It analyzes the performance of the developed anomaly detection system in detecting and classifying network traffic anomalies. The chapter discusses the accuracy, precision, recall, and other evaluation metrics of the system, highlighting its strengths and areas for improvement. Chapter 5 concludes the thesis by summarizing the key findings, implications, and contributions of the research. It discusses the practical applications of the developed anomaly detection system in real-world network security scenarios and proposes future research directions to enhance the effectiveness and efficiency of anomaly detection in network traffic using machine learning techniques. In conclusion, this research project aims to address the critical need for robust anomaly detection systems in network traffic using advanced machine learning techniques. By developing a reliable and efficient anomaly detection system, this study contributes to enhancing the security and resilience of network systems against evolving cyber threats and attacks.